Lýsing:
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated. This unique computational approach ensures that you understand enough of the details to make reasonable choices and interpretations in your own modeling work.
The text presents causal inference and generalized linear multilevel models from a simple Bayesian perspective that builds on information theory and maximum entropy. The core material ranges from the basics of regression to advanced multilevel models. It also presents measurement error, missing data, and Gaussian process models for spatial and phylogenetic confounding. The second edition emphasizes the directed acyclic graph (DAG) approach to causal inference, integrating DAGs into many examples.
The new edition also contains new material on the design of prior distributions, splines, ordered categorical predictors, social relations models, cross-validation, importance sampling, instrumental variables, and Hamiltonian Monte Carlo. It ends with an entirely new chapter that goes beyond generalized linear modeling, showing how domain-specific scientific models can be built into statistical analyses.
Features Integrates working code into the main text Illustrates concepts through worked data analysis examples Emphasizes understanding assumptions and how assumptions are reflected in code Offers more detailed explanations of the mathematics in optional sections Presents examples of using the dagitty R package to analyze causal graphs Provides the rethinking R package on the author's website and on GitHub.
Annað
- Höfundur: Richard McElreath
- Útgáfa:2
- Útgáfudagur: 2020-03-13
- Hægt að prenta út 2 bls.
- Hægt að afrita 2 bls.
- Format:ePub
- ISBN 13: 9780429639142
- Print ISBN: 9780367139919
- ISBN 10: 0429639147
Efnisyfirlit
- Cover
- Half Title
- Series Page
- Title Page
- Copyright Page
- Table of Contents
- Preface to the Second Edition
- Preface
- Audience
- Teaching strategy
- How to use this book
- Installing the rethinking R package
- Acknowledgments
- Chapter 1. The Golem of Prague
- 1.1. Statistical golems
- 1.2. Statistical rethinking
- 1.3. Tools for golem engineering
- 1.4. Summary
- Chapter 2. Small Worlds and Large Worlds
- 2.1. The garden of forking data
- 2.2. Building a model
- 2.3. Components of the model
- 2.4. Making the model go
- 2.5. Summary
- 2.6. Practice
- Chapter 3. Sampling the Imaginary
- 3.1. Sampling from a grid-approximate posterior
- 3.2. Sampling to summarize
- 3.3. Sampling to simulate prediction
- 3.4. Summary
- 3.5. Practice
- Chapter 4. Geocentric Models
- 4.1. Why normal distributions are normal
- 4.2. A language for describing models
- 4.3. Gaussian model of height
- 4.4. Linear prediction
- 4.5. Curves from lines
- 4.6. Summary
- 4.7. Practice
- Chapter 5. The Many Variables & The Spurious Waffles
- 5.1. Spurious association
- 5.2. Masked relationship
- 5.3. Categorical variables
- 5.4. Summary
- 5.5. Practice
- Chapter 6. The Haunted DAG & The Causal Terror
- 6.1. Multicollinearity
- 6.2. Post-treatment bias
- 6.3. Collider bias
- 6.4. Confronting confounding
- 6.5. Summary
- 6.6. Practice
- Chapter 7. Ulysses’ Compass
- 7.1. The problem with parameters
- 7.2. Entropy and accuracy
- 7.3. Golem taming: regularization
- 7.4. Predicting predictive accuracy
- 7.5. Model comparison
- 7.6. Summary
- 7.7. Practice
- Chapter 8. Conditional Manatees
- 8.1. Building an interaction
- 8.2. Symmetry of interactions
- 8.3. Continuous interactions
- 8.4. Summary
- 8.5. Practice
- Chapter 9. Markov Chain Monte Carlo
- 9.1. Good King Markov and his island kingdom
- 9.2. Metropolis algorithms
- 9.3. Hamiltonian Monte Carlo
- 9.4. Easy HMC: ulam
- 9.5. Care and feeding of your Markov chain
- 9.6. Summary
- 9.7. Practice
- Chapter 10. Big Entropy and the Generalized Linear Model
- 10.1. Maximum entropy
- 10.2. Generalized linear models
- 10.3. Maximum entropy priors
- 10.4. Summary
- Chapter 11. God Spiked the Integers
- 11.1. Binomial regression
- 11.2. Poisson regression
- 11.3. Multinomial and categorical models
- 11.4. Summary
- 11.5. Practice
- Chapter 12. Monsters and Mixtures
- 12.1. Over-dispersed counts
- 12.2. Zero-inflated outcomes
- 12.3. Ordered categorical outcomes
- 12.4. Ordered categorical predictors
- 12.5. Summary
- 12.6. Practice
- Chapter 13. Models With Memory
- 13.1. Example: Multilevel tadpoles
- 13.2. Varying effects and the underfitting/overfitting trade-off
- 13.3. More than one type of cluster
- 13.4. Divergent transitions and non-centered priors
- 13.5. Multilevel posterior predictions
- 13.6. Summary
- 13.7. Practice
- Chapter 14. Adventures in Covariance
- 14.1. Varying slopes by construction
- 14.2. Advanced varying slopes
- 14.3. Instruments and causal designs
- 14.4. Social relations as correlated varying effects
- 14.5. Continuous categories and the Gaussian process
- 14.6. Summary
- 14.7. Practice
- Chapter 15. Missing Data and Other Opportunities
- 15.1. Measurement error
- 15.2. Missing data
- 15.3. Categorical errors and discrete absences
- 15.4. Summary
- 15.5. Practice
- Chapter 16. Generalized Linear Madness
- 16.1. Geometric people
- 16.2. Hidden minds and observed behavior
- 16.3. Ordinary differential nut cracking
- 16.4. Population dynamics
- 16.5. Summary
- 16.6. Practice
- Chapter 17. Horoscopes
- Endnotes
- Bibliography
- Citation index
- Topic index
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- Gerð : 208
- Höfundur : 15199
- Útgáfuár : 2020
- Leyfi : 379